Optimus Is Born - Tesla, August 2021

To see other books: Summaries

    
The friendly robot

Musk's interest in creating a humanoid robot stretched back to the fascination and fear he felt about artificial intelligence. The possibility that someone might create, intentionally or inadvertently, AI that could be harmful to humans led him to start OpenAI in 2014. It also led him to push related endeavors, including self- driving cars, a neural network training supercomputer known as Dojo, and Neuralink chips that could be implanted in brains to create a very intimate symbiotic relationship between humans and machines.

An ultimate expression of safe AI, especially for someone who imbibed sci-fi as a kid, would be creating a humanoid robot, one that could process visual inputs and learn to perform tasks without violating Asimov's law that a robot shall not harm humanity or any human. While OpenAI and Google were focusing on creating text-based chatbots, Musk decided to focus on artificial intelligence systems that operated in the physical world, such as robots and cars. “If you can create a self-driving car, which is a robot on wheels, then you can make a robot on legs as well,” Musk said.

In early 2021, Musk began mentioning at his executive meetings that Tesla should get serious about building a robot, and at one point he played for them a video of the impressive ones that Boston Dynamics were designing. “Humanoid robots are going to happen, like it or not,” he said, “and we should do it so we can guide it in a good direction.” The more he talked about it, the more excited he got.

“This has the potential to be the far biggest thing we ever do, even bigger than a self-driving car,” he told his chief designer, Franz von Holzhausen.

“Once we hear a recurring theme from Elon, we start working on it,” von Holzhausen says. They began meeting in the Tesla design studio in Los Angeles, where the Cybertruck and Robotaxi models were on display. Musk gave the specs: the robot should be about five-foot-eight, with an elfish and androgenous look so it “doesn't feel like it could or would want to hurt you.” Thus was born Optimus, a humanoid robot to be made by the Tesla teams working on self-driving cars.

Musk decided that it should be announced at an event called “AI Day,” which he scheduled for Tesla's Palo Alto headquarters on August 19, 2021.

AI Day

Two days before AI Day, Musk held a prep meeting with the Tesla team virtually from Boca Chica. That day also included a meeting with the Texas Fish and Wildlife Conservation Office to get support for Starship launches, a Tesla finance meeting, a discussion of solar roof finances, a meeting about future launches of civilians, a contentious walk through the tents where Starship was being assembled, an interview for a Netflix documentary, and his second late-night visit to the tract houses where Brian Dow's team was installing solar roofs. After midnight, he got on his plane and headed for Palo Alto.
“It's draining to have to switch between so many issues,” he said when he finally relaxed on the plane. “But there are a lot of problems, and I have to solve them.” So, why was he now leaping into the world of AI and robots? “Because I'm worried about Larry Page,” he said. “I had long conversations with him about AI dangers, but he didn't get it. Now we barely speak.”

When we landed at 4 a.m., he went to a friend's house for a few hours of sleep, then to Tesla's Palo Alto headquarters to meet with the team preparing for the robot announcement. The plan was for an actress to dress up as the robot and come onstage. Musk got excited. “She will do acrobatics!” he declared, as if in a Monty Python sketch. “Can we make her do cool stuff that looks impossible? Like tap dancing with a hat and cane?”
He had a serious point: the robot should seem fun rather than frightening. As if on cue, X started dancing on the conference room table. “The kid has a real good power pack,” his father said. “He gets his software updates by walking around and looking and listening.” That was the goal: a robot that could learn to do tasks by seeing and mimicking humans.

After a few more jokes about hat-and-cane dancing, Musk began drilling down on the final specifications. “Let's make it go five miles per hour, not four, and give it power to lift a bit more weight,” he said. “We overdid making it look gentle.”
When the engineers said that they were planning to have the batteries swapped out when they ran down, Musk vetoed that idea. “Many a fool has gone down the swappable battery path, and it's usually because they have a lousy battery,” he said. “We went down that path with Tesla originally. No swappable pack. Just make the pack bigger so it can operate sixteen hours.”

After the meeting, he stayed behind in the conference room. His neck was hurting from his old Sumo wrestling accident, and he lay on the floor with an ice pack behind his head. “If we're able to produce a general-purpose robot thatcould observe you and learn how to do a task, that would supercharge the economy to a degree that's insane,” he said. “Then we may want to institute universal basic income. Working could become a choice.” Yes, and some would still be maniacally driven to do it.

Musk was in a foul mood at the next day's practice session for AI Day presentations, which would feature not only the unveiling of Optimus but also the advances Tesla was making in self-driving cars. “This is boring,” he kept saying as Milan Kovac, a sensitive Belgian engineer who ran the Autopilot and Optimus software teams, presented very technical slides. “There is too much here that is not cool. This is a recruiting event, and no one will want to join after seeing these fucking slides.”

Kovac, who had not yet mastered the art of deflecting Musk's blasts, walked back to his office and quit, throwing plans for that evening's presentation into disarray. Lars Moravy and Pete Bannon, his more seasoned and battle-hardened supervisors, stopped him as he was about to leave the building. “Let's look at your slides and see how we can fix this,” Moravy said. Kovac mentioned he could use a whiskey, and Bannon found someone in the Autopilot workshop who had some.

They drank two shots, and Kovac calmed down. “I'm going to get through the event,” he promised them. “I'm not going to let my team down.”
With the help of Moravy and Bannon, Kovac cut in half the number of his slides and rehearsed a new speech. “I sucked up my anger and brought the new slides to Elon,” he says. Musk glanced through them and said, “Yep, sure. Okay.”

Kovac got the impression that Musk did not even remember chewing him out.

The disruption caused the presentation that evening to be delayed by an hour.

It was not a very polished event. The sixteen presenters were all male. The only woman was the actress who dressed up as the robot, and she didn't do any fun hat-and-cane dance routines. There were no acrobatics. But in his slightly stuttering monotone, Musk was able to connect Optimus to Tesla's plans for self- driving cars and the Dojo supercomputer. Optimus, he said, would learn to perform tasks without needing line-by-line instructions. Like a human, it would teach itself by observing. That would transform not only our economy, he said, but the way we live.

Ref: Chapter 64, "Elon Musk" by Walter Isaacson

Tags: Book Summary,Technology,Artificial Intelligence,

39 AI Code Tools - The Ultimate Guide in 2024

To See All Articles About Technology: Index of Lessons in Technology

What are the best AI code tools in 2024?

TL;DR - As of September 2024, most programmers achieve the best results by using Cursor with Anthropic Sonnet 3.5 or OpenAI o1.

AI coding tools are becoming standard practice for many developers. And today, you’ll learn which code generators  and tools are the best ones out there for creating high-quality code with the help of artificial intelligence.

Want to learn more? Read on!

Is it possible to code with AI tools?

Yes, it is possible to code with AI tools.  In fact, leveraging AI tools for coding is not only possible, but it can also significantly enhance productivity and accuracy.

AI code is code written by artificial intelligence (AI), often times utilizing large language models (LLMs). These AI programs can write their own programs or translate from one programming language to another. They also perform tasks like offering assistance in auto-generating documentation and finding code snippets faster.

One of the most popular tools is Open AI’s Codex, an AI system that translates natural language to code. Codex powers GitHub Copilot, another popular AI code tool.

OpenAI Codex is capable of interpreting simple commands in natural language and carrying them out for the programmer. This makes it possible to build on top of the existing application with a natural language interface.

As a general-purpose programming model, OpenAI Codex can be applied to almost any programming task. That said, the tool is in beta and so results will vary.

AlphaCode by DeepMind is another tool that is shaking up the industry. Interestingly, this tool outperforms human coders in certain situations. You see, AlphaCode outperformed 45% of programmers in coding competitions with at least 5,000 participants.

However, there are problems with code generators, too. That's why AI coding tools are used to help developers become more productive and efficient, rather than to replace them entirely.

For example, a Stanford-affiliated research team found that engineers who use AI tools are more likely to cause security vulnerabilities in their apps. Plus, questions around copyright are not entirely resolved.

In other words, AI code tools are not yet completely safe to use. That said, the popularity of these tools means that they can’t be overlooked.

What is AI code written in?

AI code is written in languages supported by the AI code generator. For example, OpenAI Codex is most fluent in Python but is also quite capable in several languages, including JavaScript, Ruby, and TypeScript.

Now, let’s take a look at the best code generators out there.

The best AI code generators and AI development tools

What are some effective AI code generators? The most popular ones include OpenAI Codex, Copilot by Github,  ChatGPT by OpenAI as well as open-source models such as Llama 3.

But there are plenty of other tools out there. I’ve listed them here below, including their features, capabilities, and which companies are behind them. Let’s dive in!

Here are the best AI code generators of 2024.

1. OpenAI (ChatGPT, GPT-4, o1)

GPT-4, OpenAI's latest AI model, is a multimodal tool that excels in programming tasks. It understands and explains code, writes new code, and outperforms existing models on Python coding tasks. Despite its ability to handle complex tasks, it has limitations like reasoning errors and potential security vulnerabilities in the code it produces.  

ChatGPT is primarily a user-friendly interface developed by OpenAI that allows you to interact conversationally with advanced language models like GPT-4 and o1-mini. While it's often referred to as a model, ChatGPT is essentially the platform that enables you to generate or debug code and perform other text-based tasks by communicating with these underlying AI models.

Update May 14th: OpenAI just releaded GPT-4o - their new flagship model that’s as smart as GPT-4 Turbo and much more efficient. With 50% reduced pricing and 2x faster latency, it achieves impressive results.

Update September 16th:  o1 is a new series of AI models designed to enhance reasoning by spending more time thinking through problems before responding, excelling in complex tasks in science, coding, and math. OpenAI o1-mini is a faster, more cost-effective model particularly effective at coding, offering an affordable solution for applications that require reasoning but not extensive world knowledge. Both models are now available in ChatGPT and via the API for users to tackle complex problems efficiently.

Price: Free or $20 for GPT Plus

2. Copilot

Copilot uses publicly available code from GitHub repositories so that users can access large datasets and quickly develop accurate code. The tool detects errors in code and recommends changes to it. You can start using GitHub Copilot by installing one of the extensions in your preferred environment.

Price: $10-$19 - GitHub Copilot is free to use for verified students, teachers, and maintainers of popular open source projects.

3. AWS Bedrock

AWS Bedrock is Amazon Web Services' fully managed service that provides developers with access to a variety of powerful foundation models for building and scaling generative AI applications. For programmers, it offers APIs to interact with models like Amazon's Titan and others from leading AI startups, enabling tasks such as code generation, debugging, and text synthesis. While AWS Bedrock simplifies integrating AI into applications, it may have limitations like model accuracy and potential security vulnerabilities in generated code, so developers should exercise caution and perform thorough testing.

Pricing information can be found here

4. AlphaCode

Another AI-based code generator is Google-backed DeepMind’s AlphaCode, which gives developers access to source code from various language libraries. With AlphaCode, developers can leverage thousands of pre-made libraries, helping them connect and use third-party APIs quickly and easily. AlphaCode is not yet available to the public.

Price: No information available

5. Tabnine

Tabnine is an AI code completion tool that utilizes deep learning algorithms to provide the user with intelligent code completion capabilities. Tabnine supports several programming languages such as Java, Python, C++, and more. This tool is open-source and is used by leading tech companies like Facebook and Google.

Price: Paid plans start from $12/month per seat

6. CodeT5

CodeT5 is an open AI code generator that helps developers to create reliable and bug-free code quickly and easily. It is also open-source and provides support for various programming languages such as Java, Python, and JavaScript. CodeT5 also has an online version as well as an offline version for data security.

Price: Free

7. Polycoder

Polycoder is an open-source alternative to OpenAI Codex. It is trained on a 249 GB codebase written in 12 programming languages. With Polycoder, users can generate code for web applications, machine learning, natural language processing and more. It is well-regarded amongst programmers because of its capability of generating code quickly.

Price: Free

8. Deepcode

DeepCode is a cloud-based AI code analysis tool that automatically scans the codebase of a project and identifies potential bugs and vulnerabilities. It offers support for multiple languages such as Java, Python, and JavaScript. DeepCode is well-regarded for its accurate bug detection.

Price: No information available

9. WPCode

WPCode is an AI-driven WordPress code generator created by Isotropic. It supports both developers and non-technical WordPress creators, allowing them to quickly generate high-quality code snippets. CodeWP supports not only HTML and CSS but languages such as Java and Python. It even includes AI assistants to suggest improvements to code snippets.

Price: Starting at $49

10. AskCodi

AskCodi is a code generator that offers a full suite of development tools to help developers build and ship projects faster. With its AI-based code generation, it helps developers write better code and shorter code blocks, with fewer mistakes. AskCodi can be used to develop both web and mobile applications.

Price: Paid plans start from $7.99/month per seat

11. Codiga

Codiga is a static analysis tool that ensures code is secure and efficient. It supports popular languages like JavaScript, Python, Ruby, Kotlin, and more. With Codiga, you can test your code for vulnerabilities and security issues in real time. It also includes an auto-fixer to quickly address any issues in the code.

Price: Paid plans start from $14/month per seat

12. Visual Studio IntelliCode

Visual Studio IntelliCode is an extension of the Visual Studio Code editor created by Microsoft that provides AI-assisted development experiences to improve developer productivity. It offers smarter IntelliSense completions and helps reduce the amount of time developers spend navigating and debugging code.

Price: Starting from $45/month

13. PyCharm

PyCharm is an AI code completion tool from JetBrains which provides developers with intelligent code completion capabilities. This tool supports various programming languages such as Java, Python, and JavaScript. PyCharm is well regarded for its accuracy and can help developers reduce the amount of time spent on coding tasks.

Price: Starting from $24.90/month per seat

14. AIXcoder

AIXcoder is an AI-powered programming pair designed to aid development teams in writing code. It supports languages such as Java, Python, and JavaScript. This tool also offers a range of features such as automated routine tasks, AI-powered code completion, real-time code analysis and error checks while typing.

Price: No information available

15. Ponicode

Ponicode is an AI-powered code assistant designed to help developers optimize their coding workflow. It uses natural language processing and machine learning to generate code from user-defined descriptions. The tool is maintained by CircleCI.

Price: No information available

16. Jedi

Jedi is an open-source option for code completion in AI. It mostly functions as a plugin for editors and IDEs that use Python static analysis tools.

Price: Free

17. Wing Python IDE Pro

Created by Wingware, Wing IDE is a Python-specific software setup that combines the code editing, code navigation, and debugging mechanisms required to Code and Test Software applications. It offers various features such as an intelligent auto-completing Editor, Refactoring, Multi-Selection, and Code Snippets, which make coding much easier and more efficient.

Price: Annual licenses starting at $179/month

18. Smol Developer

Smol is an open-source artificial intelligence agent designed to function as a personal junior developer, capable of generating an entire codebase from your specific product specifications. Unlike traditional, rigid starter templates, Smol can create any kind of application based on your unique requirements. Boasting a codebase that is simple, safe, and small, it offers the perfect blend of ease-of-understanding, customization, and a helpful, harmless, and honest approach to AI development.

Price: Smol is open-source with a MIT License.

19. Cody (Sourcegraph)

Cody (not to be confused with AskCodi), Sourcegraph's AI tool, is a comprehensive coding assistant. It understands your entire codebase, answers queries, and writes code. Beyond guidance, Cody provides detailed code explanations, locates specific components, and identifies potential issues with suggested fixes. Cody works directly in VS code with an extension.

Price: Cody is free for personal use, Sourcegraph starts at $5k/year

20. CodeWhisperer (Amazon)

CodeWhisperer is a tool developed by Amazon. It offers real-time, AI-driven code suggestions and identifies potential open-source code matches for easier review. It even scans for security vulnerabilities, suggesting immediate patches. An added bonus is its commitment to code safety, always aligning with best security practices such as OWASP guidelines.

Price: Free for personal use, $19/month professional use

21. Bard (Google)

Bard can help with programming and software development tasks, including code generation, debugging and code explanation. These capabilities are supported in more than 20 programming languages including C++, Go, Java, Javascript, Python and Typescript. And you can easily export Python code to Google Colab — no copy and paste required. Bard can also assist with writing functions for Google Sheets.

Price: Google Bard is Free

22. Code Llama (Meta)

Code Llama is a set of large language models specialized for coding, built on the Llama 2 platform. It includes different models for various needs: the general-purpose Code Llama, Code Llama - Python for Python-specific tasks, and Code Llama - Instruct for instruction-based coding. These models vary in size (7B, 13B, and 34B parameters) and can handle up to 16k token inputs, with some improvements on up to 100k tokens. The 7B and 13B models also offer content-based infilling.

Code Llama’s training recipes are available on their Github repository - Model weights are also available.

23. Claude 2 & 3, 3.5 (Anthropic)

Claude 3.5 Sonnet is the latest natural language AI model introduced by Anthropic, a firm established by Dario Amodei, formerly of OpenAI. This new iteration is engineered for enhanced input and output lengths and boasts superior performance relative to its earlier version. In an internal agentic coding evaluation, Claude 3.5 Sonnet solved 64% of problems, outperforming Claude 3 Opus which solved 38%. Users can input up to 100K tokens in each prompt, which means that Claude can work over hundreds of pages of technical documentation. The earlier version, Claude 2 scored a 71.2% up from 56.0% on the Codex HumanEval, a Python coding test.

Their evaluation tests the model’s ability to fix a bug or add functionality to an open source codebase, given a natural language description of the desired improvement. When instructed and provided with the relevant tools, Claude 3.5 Sonnet can independently write, edit, and execute code with sophisticated reasoning and troubleshooting capabilities. It handles code translations with ease, making it particularly effective for updating legacy applications and migrating codebases.

A Stability AI Membership is required for commerical application

24. Stable Code 3B

Stability AI's Stable Code 3B, a new 3 billion parameter Large Language Model specialized in code completion, which is 60% smaller yet performs similarly to the larger CodeLLaMA 7b. This model, trained on diverse programming languages and software engineering-specific data, can run in real-time on modern laptops without a GPU. Stable Code 3B is part of Stability AI's Membership program and offers advanced features like Fill in the Middle capabilities and expanded context size, demonstrating state-of-the-art performance in multi-language coding tasks.

A Stability AI Membership (Starting at $20/mo) is required for commercial applications. Free for non-commercial.

25. Replit AI

Replit AI is an innovative code completion tool designed to streamline your coding experience by offering tailored suggestions that align with the context of your current file. As you delve into coding, the tool intuitively presents inline suggestions, enhancing your efficiency and accuracy. Additionally, Replit AI offers advanced features such as the ability to refine suggestions through code comments, the application of prompt engineering for more relevant results, and the flexibility to toggle the code completion feature on or off within the editor settings, ensuring a customized coding environment tailored to your preferences.

Replit AI is available in Replit's Free tier (Limited) and in their Core tier (Advanced Model).  

26. Plandex

Plandex employs persistent agents that tackle extensive tasks spanning numerous files and involving multiple steps. It segments sizable tasks into manageable subtasks, executing each in sequence until the entire task is accomplished. This tool aids in clearing your backlog, navigating new technologies, overcoming obstacles, and reducing the time spent on mundane activities.

Plandex is open-source on Github

27. Meta AI (Meta Lama 3)

Meta has launched Meta AI, powered by the Llama 3 model with 70 billion parameters.  The model positions itself as a powerful asset for improving application functionalities, but it does not match the customization and transparency of more advanced models like GPT-4 Turbo and Claude Opus. The benefits of Meta's approach to open-source AI are multifaceted, including attracting top talent, leveraging community contributions, fostering standardization and lower costs, building goodwill, and aligning with business models that do not rely solely on AI products.  While it is described as "open weight," providing access to the model's weights, it does not include the full toolkit necessary for reproduction. They also co-developed Llama 3 with torchtune, the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs.

Moreover, Meta is also currently pretraining a 405B parameter model, signaling an ambitious expansion of its AI capabilities. This larger model, set to be released later, promises even more powerful functionalities and potential industry leadership if it surpasses current leaders like GPT-4 and Claude Opus. Such a development could reshape industry standards and perceptions, especially against competitors who guard their models under the guise of safety concerns. This bold move by Meta not only showcases their commitment to advancing AI technology but also challenges the industry's more cautious narratives around the sharing and utilization of AI models, setting new benchmarks for what’s achievable in AI development.

28. MetaGPT

Not to be confused with Meta AI, MetaGPT is a tool that automates the generation of software development outputs such as user stories, competitive analysis, requirements, data structures, APIs, and documents from a single line of input. It integrates roles typically found in a software company—product managers, architects, project managers, and engineers—into its workflow. These roles are executed by large language models (LLMs) following detailed Standard Operating Procedures (SOPs). The core philosophy behind MetaGPT is "Code = SOP(Team)," emphasizing the application of SOPs to organize and direct the work of its LLM teams. This structure aims to mimic the entire process of a software company, simplifying and automating complex tasks.

MetaGPT is MIT licensed and open-source

29. AutoRegex

AutoRegex is my favorite tool to translate natural language to regex. If you're like me, you wiped all traces of regex syntax from your memory the moment ChatGPT released - this helps!

30. llama.cpp

Llama.cpp is designed to facilitate LLM inference with optimal performance and minimal initial setup across various hardware, both locally and in the cloud. It is implemented in plain C/C++ without dependencies and features extensive support for Apple silicon through ARM NEON, Accelerate, and Metal frameworks. It also supports AVX, AVX2, and AVX512 for x86 architectures and offers integer quantization from 1.5 to 8 bits to enhance inference speed and reduce memory consumption. For NVIDIA GPUs, llama.cpp includes custom CUDA kernels, with AMD GPU support through HIP. Additionally, it supports Vulkan, SYCL, and partial OpenCL backends and can perform hybrid CPU+GPU inference to manage models that exceed VRAM capacity.

31. Aider

Aider is a  command line tool  allowing you to pair program with LLMs directly in your terminal. It seamlessly integrates with your local git repository, editing code directly in your source files and crafting smart commit messages for each change.

Aider is open-source on Github

32. Codestral (Mistral)

A model fluent in 80+ programming languages, Codestral, is Mistrral's first-ever code model. Codestral is an open-weight generative AI model explicitly designed for code generation tasks. It helps developers write and interact with code through a shared instruction and completion API endpoint. As it masters code and English, it can be used to design advanced AI applications for software developers.

Codestral is a 22B open-weight model licensed under the new Mistral AI Non-Production License, which means that you can use it for research and testing purposes. Codestral can be downloaded on HuggingFace

Update July 16th: Codestral Mamba release:  For easy testing, they made Codestral Mamba available on la Plateforme (codestral-mamba-2407), alongside its big sister, Codestral 22B. While Codestral Mamba is available under the Apache 2.0 license, Codestral 22B is available under a commercial license for self-deployment or a community license for testing purposes.

33. Cursor

Cursor is an AI-enhanced code editor designed to boost productivity by enabling developers to interact with their codebase through conversational AI and natural language commands. It includes features like Copilot++, which predicts your next code edit, and Cmd-K, which allows code modifications through simple prompts.

You can try Cursor for free

34. Warp

Warp is a modern, Rust-based terminal with AI built in. Type ‘#’ on your command line and start describing the command you want to run using natural language. Warp will load AI Command Suggestions as you type.

Warp AI is free to use up to 40 requests per user per month. You can create a Team and upgrade to a Team plan to unlock higher Warp AI request limits. Visit the pricing page to learn more.

35. CodiumAI

CodiumAI is a trending tool that developers can use to enhance their coding experience with the power of AI.

Key features: When compared to the other tools, CodiumAI provides a set of unique features:

Precise code suggestions: CodiumAI thoroughly analyzes your code, providing tailored suggestions. These include adding docstrings, refining exception handling, and implementing best practices, directly improving your code’s quality.
Code explanation: This tool offers detailed descriptions of your source code or snippets, breaking down each component and offering insights and sample usage scenarios to enhance code comprehension.
Automated test generation: Testing is essential in large codebases. CodiumAI simplifies this by swiftly generating accurate and reliable unit tests without manual intervention, saving significant time and effort and ensuring thorough testing of your codebase.
Code behavior coverage: Comprehensive testing means covering all possible code behaviors. CodiumAI’s “Behavior Coverage” feature generates test cases covering various code behaviors and seamlessly applies related changes to your source code.
Streamlined collaboration: CodiumAI facilitates teamwork by enabling seamless collaboration among developers. Its Git platform integration allows for sharing and reviewing code suggestions and test cases within your development team, promoting efficient workflows and code quality.
Seamless implementation: With CodiumAI’s intelligent auto-completion agent, implementation becomes effortless. It seamlessly integrates with your task plans, ensuring smooth execution from concept to completion of your code.
Multiple language and IDE support: CodiumAI supports popular programming languages such as Python, JavaScript, and TypeScript while seamlessly integrating with leading IDEs, including VSCode, WebStorm, IntelliJ IDEA, CLion, PyCharm, and JetBrains.
Pricing

The pricing of CodiumAI offers free code integrity for developers at $0/user per month, while teams can access optimized collaboration for $19/user per month.

36. MutableAI

MutableAI is a tool that revolutionizes the coding experience with features such as AI autocomplete, one-click production code enhancements, prompt-driven development, test generation, and extensive language and IDE integration, empowering developers to write code more efficiently and effectively.

Key features

Here are the key features of MutableAI:

AI Autocomplete: Minimize time spent on boilerplate code and searching for solutions on Stack Overflow with specialized neural networks providing intelligent code suggestions.
Production Quality Code: Refactor, document, and add types to your code effortlessly, ensuring high-quality code output.
Prompt-driven Development: Interact directly with the AI by giving instructions to modify your code, enabling a more intuitive and interactive coding experience.
Test Generation: Automatically generate unit tests using AI and metaprogramming techniques, ensuring comprehensive test coverage for your code.
Language and IDE Integration: Supports popular languages like Python, Go, JavaScript, TypeScript, Rust, Solidity, and more, as well as integration with IDEs like JetBrains and Visual Studio (VS) Code.
Pricing

MutableAI’s basic plan offers $2 per repo per month, while its premium plan offers $15 per repo per month.

37. Figstack

Figstack is an innovative AI tool that provides developers with various features to improve code understanding, translation, documentation, and optimization. Figstack caters to developers at all levels, from beginners looking to understand complex code to experienced professionals aiming to automate tedious tasks like writing documentation or measuring code efficiency.

Key features

Code explanation in natural language: This feature helps users easily understand the code written in any language by translating it into clear, natural language descriptions.
Cross-Language code translation: Developers can easily convert code from one programming language to another. This simplifies the process of porting applications across different technology stacks.
Automated function documentation: Figstack automatically generates detailed docstrings that describe the function’s purpose, parameters, and return values, ensuring that your code is always readable, maintainable, and well-documented.
Time complexity analysis: The tool helps developers assess the efficiency of their code in Big O notation, pinpoint bottlenecks, and optimize their code for better performance by identifying the time complexity of a program.
Pricing

Figstack is free to use and includes most of the essential features.

38. CodeGeeX

CodeGeeX is an AI-powered code generation tool designed to assist developers in writing, completing, and optimizing code more efficiently. It leverages deep learning models trained on a wide variety of programming languages and codebases, where it can provide context-aware code suggestions, complete code snippets, and even generate entire functions or modules.

Key features

Code generation and completion: CodeGeeX offers accurate code generation capabilities based on natural language descriptions. Also, it can complete the current line or multiple lines ahead, making the development process faster.
Code translation: Developers can effortlessly convert their code from one programming language to another.
Automated comment generation: The tool saves time by automatically generating line-level comments, which helps improve code readability and maintainability.
AI chatbot: The AI chatbot in CodeGeeX provides quick answers to technical questions directly within the development environment instead of having developers find solutions on the internet.
Wide IDE and language support: CodeGeeX supports various popular IDEs, including Visual Studio Code, JetBrains IDEs and multiple programming languages, such as Python, C++, JavaScript, and Go.
Pricing

CodeGeeX offers their plugin completely free for individual users. If there are more advanced requirements, they provide an enterprise plan.

39. Codeium

One I personally use.

Millions of engineers, including our own, use these features every single day.
Autocomplete
Autocomplete faster than thought. Codeium's generative code can save you time and help you ship products faster.

Command
Give instructions in your editor to perform inline refactors, whether it is generating code, adding comments, or something even more complex.

Chat
Generate boilerplate, refactor code, add documentation, explain code, suggest bug fixes, and so much more. Powered by the largest models, optimized for coding workflows and Codeium's industry-leading reasoning engine.

Context
All of Codeium's features are powered by an industry-leading context awareness and reasoning engine. With full repository and multi repository codebase awareness, Codeium provides 35% more value purely from providing more grounded results. 

References

• code submit
• codium.ai

Tags: Technology,Artificial Intelligence,Generative AI,Large Language Models,Python,JavaScript,

Cisco’s second layoff of 2024 affects thousands of employees

To See All Articles About Layoffs / Management: Index of Layoff Reports

U.S. tech giant Cisco has let go of thousands of employees following its second layoff of 2024. 

The technology and networking company announced in August that it would reduce its headcount by 7%, or around 5,600 employees, following an earlier layoff in February, in which the company let go of about 4,000 employees. 

As TechCrunch previously reported, Cisco employees said that the company refused to say who was affected by the layoffs until September 16. Cisco did not give a reason for the month-long delay in notifying affected staff. One employee told TechCrunch at the time that Cisco’s workplace had become the “most toxic environment” they had worked in.

TechCrunch has learned that the layoffs also affect Talos Security, the company’s threat intelligence and security research unit. 

Cisco said in its August statement that its second layoff of the year would allow the company to “invest in key growth opportunities and drive more efficiencies.” On the same day, Cisco published its most recent full-year earnings report, in which the company said 2024 was its “second strongest year on record,” citing close to $54 billion in annual revenue.

Cisco chief executive Chuck Robbins made close to $32 million in total executive compensation during 2023, according to the company’s filings.

When reached by email, Cisco spokesperson Lindsay Ciulla did not provide comment, or say if Cisco’s executive leadership team planned to reduce their compensation packages following the layoffs.

Are you affected by the Cisco layoffs? Get in touch. You can contact this reporter on Signal and WhatsApp at +1 646-755-8849, or by email. You can send files and documents via SecureDrop.

A look at Cisco’s response to the current economic climate and transition trajectory leading to significant layoffs:

Cisco’s focus on subscription-based services

Cisco's $28 billion acquisition of Splunk in March signals a strategic shift towards subscription-based services. This move marked a significant shift for Cisco, traditionally known for networking equipment, as it entered the competitive cybersecurity market alongside players like Palo Alto Networks, Check Point, CrowdStrike, and Microsoft, as ET followed this development.

Cisco’s funding to AI startups

Since 2018, Cisco has been actively involved in the AI space, acquiring Accompany and CloudCherry to expand its presence in this rapidly growing technology. In 2019, the company launched the Silicon One ASIC chip, offering speeds of 25.6 Tbit/s, directly competing with Intel and Nvidia.

Cisco has allocated $1 billion to fund AI startups.
Earlier in February, Cisco partnered with Nvidia. The latter agreed to use Cisco's ethernet with its own technology that is widely used in data centers and AI applications. In June, Cisco invested in AI startups like Cohere, Mistral AI, and Scale AI. The company announced that it had made 20 acquisitions and investments related to AI in recent years.

Focus on emerging technologies

Cisco offers data center technologies like the Unified Computing System (UCS) and Nexus switches, designed to support modern data center and cloud environments. Additionally, their collaboration tools, such as WebEx and Cisco Jabber, enhance communication and productivity.

Shifting focus on cybersecurity

Since 2013, with the acquisition of Sourcefire, a network security and threat detection provider Cisco strengthened its security portfolio. Open DNS acquired in 2015, provides cloud based threat detection and prevention. CloudLock, a cloud security solutions provider for $293 million protects users and data in cloud environments. Duo Security, for $2.35 billion, provides cloud based authentication and access control.

References

• TechCrunch
• economic times

Tags: Technology,Layoffs,Management,Artificial Intelligence,

How to use AI for coding the right way

To See All Articles About Technology: Index of Lessons in Technology

Talk on Artificial Intelligence with Computer Science and Engg. Students

Talk on Artificial Intelligence

• Introduction
• History
• Current Status
• Future of AI
• Challenges of AI
• Pros and Cons.

1. INTRODUCTION

1.1 What is Artificial Intelligence (AI)?

• Artificial Intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, understanding natural language, speech recognition, and visual perception.

1.2 Key Concepts in AI

1.2.1 Machine Learning (ML)

• ML is a subset of AI that focuses on the development of algorithms allowing computers to learn from data.
• It involves the creation of models that can make predictions or decisions without being explicitly programmed.

Neural Networks

• Inspired by the human brain, neural networks are a fundamental part of deep learning—a subset of machine learning.
• They consist of layers of interconnected nodes (neurons) that process and analyze information.

Natural Language Processing (NLP)

• NLP enables computers to understand, interpret, and generate human language.
• Applications include language translation, sentiment analysis, and chatbots.

Computer Vision

• This field focuses on teaching machines to interpret and make decisions based on visual data, such as images or videos.
• Applications include image recognition, object detection, and autonomous vehicles.

Robotics

• AI plays a crucial role in robotics, allowing machines to perceive their environment and make intelligent decisions.
• Examples include robotic process automation and autonomous robots.

1.3 How to Get Started

1. Foundational Knowledge

Strengthen your programming skills, particularly in languages like Python and C++.

Familiarize yourself with algorithms, data structures, and computer architecture.

2. Learn Machine Learning Basics

Start with the basics of machine learning, understanding concepts like supervised learning, unsupervised learning, and reinforcement learning.

3. Hands-On Projects

Practical experience is crucial. Work on small AI projects to apply your knowledge and build a portfolio.

Explore platforms like Kaggle for real-world datasets and challenges.

4. Explore Specializations

AI is vast, so explore different specializations like computer vision, natural language processing, or reinforcement learning to find your interests.

5. Online Courses and Resources

Enroll in online courses from platforms like Coursera, edX, or Udacity. Popular courses include Andrew Ng's Machine Learning on Coursera.

6. Stay Updated

AI is constantly evolving. Follow reputable blogs, research papers, and conferences to stay informed about the latest developments.

7. Networking

Connect with AI communities, attend meetups, and engage in online forums. Networking helps you learn from others and stay motivated.

2. HISTORY

1. The Birth of AI (1950s)

• The term "Artificial Intelligence" was coined by computer scientist John McCarthy in 1956 during the Dartmouth Conference.
• McCarthy, along with Marvin Minsky, Nathaniel Rochester, and Claude Shannon, is considered one of the "founding fathers" of AI.

2. Early AI Concepts:

• Logic Theorist (1956): Created by Allen Newell and Herbert A. Simon, it was the first AI program, designed to mimic human problem-solving skills.
• General Problem Solver (1957): Another creation by Newell and Simon, this program could solve a variety of problems.

3. AI Winters (1970s-1980s):

• Despite early optimism, progress in AI faced challenges, leading to periods known as "AI winters" where funding and interest declined.
• Limited computational power and the complexity of AI tasks contributed to these setbacks.

4. Expert Systems (1970s-1980s):

• During AI winters, focus shifted to expert systems—programs that emulated the decision-making abilities of a human expert.
• MYCIN (1976), an expert system for diagnosing bacterial infections, was a notable success.

5. Rise of Machine Learning (1980s-1990s):

• AI research saw a resurgence with a focus on machine learning techniques.
• Backpropagation, a key algorithm for training artificial neural networks, was developed in the 1980s.

6. 1997: Deep Blue vs. Garry Kasparov:

• IBM's Deep Blue, a chess-playing computer, defeated world champion Garry Kasparov, showcasing the potential of AI in strategic decision-making.

7. 2000s: Big Data and the Rise of Data-Driven AI:

• The availability of large datasets and increased computing power fueled advancements in data-driven AI, including machine learning and statistical modeling.

8. Deep Learning Revolution (2010s-Present):

• Deep learning, a subset of machine learning using neural networks with multiple layers, led to breakthroughs in image and speech recognition.
• Successes include the development of convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

9. AI in Everyday Life:

• AI technologies have become integral to daily life, with applications in virtual assistants, recommendation systems, autonomous vehicles, and more.

10. Ethical and Societal Implications

• As AI continues to advance, discussions around ethics, bias, transparency, and responsible AI development have gained prominence.

Future Directions

• Ongoing research explores explainable AI, quantum computing's impact on AI, and the intersection of AI with other fields like robotics and bioinformatics.

3. CURRENT STATUS

1. Everyday AI:

• You're probably using AI more than you realize! Virtual assistants like Siri or Alexa, recommendation algorithms on streaming platforms, and even social media feeds that show you personalized content—all these use AI to understand and respond to your preferences.

2. Machine Learning Magic:

• At the heart of AI is something called "Machine Learning" (ML). Imagine giving a computer the ability to learn from examples and make decisions on its own. ML is like training a computer to recognize patterns and make predictions.

3. Deep Learning

• A cool part of Machine Learning is "Deep Learning," where computers use artificial neural networks (inspired by the human brain) to process information. This has made breakthroughs in image and speech recognition. It's why your phone can recognize your face or your voice command.

4. Smart Machines

• AI is making machines smarter. Think about self-driving cars—they use AI to understand the road, make decisions, and navigate safely. That's a big deal and involves a lot of complex AI algorithms.

5. Problem Solvers:

• AI is excellent at solving problems. It can analyze tons of data quickly to find patterns or help with decision-making. This is handy in areas like healthcare for diagnosing diseases or in finance for predicting market trends.

6. Teaching Computers to Talk:

• Natural Language Processing (NLP) is another AI field. It's about teaching computers to understand and generate human language. Chatbots, language translation apps—they all use NLP.

7. AI Challenges

• But, it's not all magic. AI faces challenges like biases in data and the need for huge amounts of data to learn effectively. Imagine if you were only taught one perspective; you might not have a complete understanding. Computers can have the same issue.

8. Future Possibilities:

• The future of AI is exciting! Researchers are working on making AI more explainable and fair. Also, people are exploring how AI can work with other technologies like robotics to create even more amazing things.

9. Your Role

• As a computer science student, you can be part of this AI journey. You might develop new algorithms, improve existing ones, or create applications that use AI to solve real-world problems. It's like being a wizard but with computers!

10. Responsibility Matters

• Lastly, with great power comes great responsibility. Understanding the ethical implications of AI is crucial. As you dive into this field, consider the impact your work can have on society and strive to create technology that benefits everyone.

4. FUTURE OF AI

1. AI Everywhere

• Imagine AI becoming a helpful friend in almost everything you do. From your home to your workplace, AI might assist you in various tasks, making life more convenient.

2. Smarter Devices

• Your devices will get even smarter. Your phone might understand you so well that it anticipates your needs before you ask. It's like having a personal assistant that knows you really, really well.

3. Healthcare Revolution

• AI could play a big role in healthcare. Imagine doctors having super-smart AI tools to help them diagnose diseases faster and more accurately. This could mean quicker and more effective treatments for patients.

4. Self-Improving AI:

• Picture an AI that doesn't just do what it's programmed for but learns and improves itself over time. It's like a student getting better and better at a subject with each passing day.

5. Creative AI:

• AI might not just be good at logical stuff but also creative tasks. Think about AI-generated art, music, or even writing. It's like having a robot friend who's an amazing artist.

6. Smart Cities:

• Entire cities could become smarter with AI. Traffic lights might adjust based on real-time traffic, energy usage might be optimized automatically, making our cities more efficient and eco-friendly.

7. Communication Breakthroughs:

• Language barriers might become a thing of the past. AI could translate languages in real-time, allowing people from different parts of the world to communicate effortlessly.

8. Exploration Beyond Earth

• AI might help us explore space. Robots with advanced AI could be sent to distant planets, helping us understand the universe better.

9. Ethical Considerations:

• But, as AI becomes more powerful, we need to think about its impact on society. How do we make sure it's used ethically and doesn't harm anyone? It's like making sure our superhero (AI) uses its powers for good.

5. CHALLENGES OF AI

1. Bias and Fairness:

• Imagine teaching a robot using old textbooks that have outdated information. If we train AI systems with biased data, they might make unfair decisions, just like a robot following outdated rules.

2. Lack of Common Sense:

• AI doesn't have common sense like humans. It might make mistakes because it doesn't understand the world the way we do. Picture an AI trying to understand jokes or sarcasm—it's like explaining humor to a robot.

3. Needing a Lot of Data:

• AI learns from examples, like a student learning from lots of textbooks. But, what if there aren't enough examples? AI might struggle to understand or make good decisions. It's like trying to learn a new game with only a few moves to study.

4. Explanations and Transparency:

• Sometimes, AI decisions seem like magic. Understanding why AI made a specific choice can be tough. It's like having a friend who gives you a surprising answer but can't explain how they arrived at it.

5. Security Concerns:

• Just like protecting your computer from viruses, we need to protect AI from "bad actors." If someone tricks an AI into making wrong decisions, it could have serious consequences.

6. Job Displacement Worries

• AI is great at repetitive tasks, but some worry it might take over jobs. Picture a robot doing your homework—great for the robot, but not so great for you.

7. Ethical Dilemmas

• Imagine an AI-powered car having to make a split-second decision in an emergency. What choice should it make? These ethical questions are like giving a robot a moral compass and hoping it makes the right call.

8. Overreliance on AI

• Relying too much on AI can be risky. If we trust it blindly, we might forget our own skills. It's like letting a GPS guide you everywhere—you might lose the ability to find your way without it.

9. Constant Learning

• AI needs to keep learning to stay relevant. It's like studying for a test, but the test is always changing. If AI doesn't keep up, it might become outdated and less useful.

10. Privacy Issues:

• AI often uses a lot of personal data. If not handled carefully, it's like having someone know everything about you. We need to make sure our data is protected, just like keeping our secrets safe.

6. PROS AND CONS

Pros (Advantages)

Efficiency Boost:

Pro: AI can perform repetitive tasks quickly and accurately, saving time and resources. It's like having a super-fast and tireless assistant.

Data Analysis:

Pro: AI can analyze vast amounts of data to identify patterns and trends that humans might miss. It's like having a detective that can sift through mountains of information in no time.

Automation:

Pro: AI enables automation of mundane tasks, freeing humans to focus on more creative and complex activities. It's like having a robot to handle routine chores.

Precision and Accuracy:

Pro: In fields like medicine and manufacturing, AI can perform tasks with high precision and accuracy, reducing errors. It's like having a surgeon with a perfect, steady hand.

24/7 Availability:

Pro: AI systems can work around the clock without needing breaks, providing continuous service. It's like having a tireless worker who never sleeps.

Innovation and Creativity:

Pro: AI can assist in creative tasks, generating new ideas, art, or music. It's like having a brainstorming partner that thinks outside the box.

Personalization:

Pro: AI can personalize experiences, like recommending movies or products based on your preferences. It's like having a knowledgeable friend who knows your taste perfectly.

Assistance in Healthcare:

Pro: AI can help in medical diagnoses and research, improving patient care. It's like having an additional expert in the medical team.

Cons (Disadvantages)

Bias and Fairness:

Con: AI systems can inherit biases from the data they are trained on, leading to unfair outcomes. It's like a mirror reflecting the biases present in society.

Lack of Common Sense:

Con: AI lacks human-like understanding and common sense, sometimes leading to misinterpretations. It's like explaining a joke to someone who takes it literally.

Job Displacement:

Con: Automation by AI may lead to job displacement in certain industries, affecting employment. It's like a robot taking over tasks traditionally done by humans.

Security Concerns:

Con: AI systems can be vulnerable to hacking and misuse, posing security risks. It's like a powerful tool that needs careful handling to prevent misuse.

Ethical Dilemmas:

Con: AI may face moral and ethical decisions, raising questions about responsibility and accountability. It's like giving a machine the ability to make moral choices.

Overreliance:

Con: Overreliance on AI without understanding its limitations can lead to dependency issues. It's like relying too much on a GPS and losing the ability to navigate without it.

Constant Learning Curve:

Con: AI systems need continuous learning and updates to stay relevant, requiring ongoing resources. It's like having to constantly upgrade your computer to keep it effective.

Privacy Issues:

Con: AI often relies on vast amounts of personal data, raising concerns about privacy and data protection. It's like having someone know too much about your personal life.

Tags: Technology,Artificial Intelligence,

Deep Learning Roadmap A Step-by-Step Guide to Learning Deep Learning

Introduction

Deep Learning, a subfield of Artificial Intelligence, has made astounding strides in recent years, powering everything from image recognition to language translation. If you're eager to embark on your journey into the world of Deep Learning, it's essential to have a roadmap. In this article, we'll provide you with a concise guide on the key milestones and steps to navigate as you master the art of Deep Learning.

Deep Learning Roadmap

Step 1: The Foundation - Understand Machine Learning Basics

Before diving deep, ensure you have a solid grasp of Machine Learning concepts. Familiarize yourself with supervised and unsupervised learning, regression, classification, and model evaluation. Books like "Machine Learning for Dummies" can be a great starting point.

Step 2: Python Proficiency

Python is the lingua franca of Deep Learning. Learn Python and its libraries, particularly NumPy, Pandas, and Matplotlib. Understanding Python is crucial as it's the primary language for developing Deep Learning models.

Step 3: Linear Algebra and Calculus

Deep Learning involves complex mathematics. Brush up on your linear algebra (vectors, matrices, eigenvalues) and calculus (derivatives, gradients) as they form the foundation of neural network operations.

Step 4: Dive into Neural Networks

Start with understanding the basics of neural networks. Learn about artificial neurons, activation functions, and feedforward neural networks. The book "Deep Learning" by Ian Goodfellow is an excellent resource.

Step 5: Convolutional Neural Networks (CNNs)

For image-related tasks, CNNs are essential. Explore how they work, learn about convolution, pooling, and their applications in image recognition. Online courses like Stanford's CS231n provide excellent materials.

Step 6: Recurrent Neural Networks (RNNs)

RNNs are crucial for sequential data, such as natural language processing and time series analysis. Study RNN architectures, vanishing gradient problems, and LSTM/GRU networks.

Step 7: Deep Dive into Deep Learning Frameworks

Become proficient in popular Deep Learning frameworks like TensorFlow and PyTorch. These libraries simplify building and training complex neural networks.

Step 8: Projects and Hands-On Practice

Apply what you've learned through projects. Start with simple tasks like digit recognition and progressively tackle more complex challenges. Kaggle offers a platform for real-world practice.

Step 9: Natural Language Processing (NLP)

For text-related tasks, delve into NLP. Learn about word embeddings, recurrent models for text, and pre-trained language models like BERT.

Step 10: Advanced Topics

Explore advanced Deep Learning topics like Generative Adversarial Networks (GANs), Reinforcement Learning, and transfer learning. Stay updated with the latest research through journals, conferences, and online courses.

Step 11: Model Optimization and Deployment

Understand model optimization techniques to make your models efficient. Learn how to deploy models in real-world applications using cloud services or on-device deployment.

Step 12: Continuous Learning

Deep Learning is a rapidly evolving field. Stay up-to-date with the latest research papers, attend conferences like NeurIPS and CVPR, and join online forums and communities to learn from others.

Conclusion

The Deep Learning roadmap is your guide to mastering this exciting field. Remember that the journey may be challenging, but it's immensely rewarding. By building a strong foundation, exploring key neural network architectures, and constantly seeking to expand your knowledge, you'll be well on your way to becoming a proficient Deep Learning practitioner. Happy learning!

References:

Full Stack Data Science with Python Course on Github